Learn by building, with your own agent
Workshops, not tutorials.
Every workshop here is self-paced and gated: a real artifact you build with your
own coding-agent harness, checked against a rubric or a deterministic test, no
"read this, then move on." Each one is its own repo, its own site, and its own
honest build log.
Learn agentic engineering by running it in a harness, not reading about it.
5 of 5 core exercises real, reviewed, and evidenced
- agentic-engineering
- context-engineering
- coding-agents
- harness-engineering
Prompt engineering, context engineering, harness engineering, and loop engineering, taught as one evolving practice instead of four unrelated topics. Every exercise runs through your own coding-agent harness; Coachgremlin frames the task and grades the attempt, never hands you the solution. Built for practitioners already using Claude Code, Codex, or Cursor daily: the only vocabulary assumed unfamiliar is "context engineering" and "loop engineering" themselves.
Build it with Claude. Prove you don't need Claude to pass.
10 of 10 modules real, doubt-driven-development reviewed, both Foundations and Professional arcs complete
- claude-certification
- exam-prep
- claude-code
- coding-agents
Anthropic's Claude Certified Architect material (Foundations, then Professional), taught the agent-native way. Every module has two parts: build a real artifact hands-on with Claude Code, then close the session and take a timed, closed-book practice checkpoint, no AI, no notes, modeled on the real exam's own blueprint and question style. The tension is the point: learning with the agent doesn't count until you can also defend it without one. Not affiliated with or endorsed by Anthropic; completing it doesn't itself credential anything.
Learn Rust the way you already work, with your agent, and let the compiler, not an opinion, be the first gate.
2 of 8 modules real (Ownership & Move Semantics, Borrowing & References)
- rust
- systems-programming
- coding-agents
Ownership, borrowing, structs and enums, generics and traits and lifetimes, error handling, concurrency, and async, taught to people who already drive a coding agent daily. Two checks on every exercise: a deterministic one (cargo test / cargo clippy, no opinion involved) and a conceptual one from Coachgremlin (did you actually work with the borrow checker, or reach for .clone()/unsafe to make it stop complaining?). The name is the pitch: not aiming to get past the borrow checker, aiming to become native to it.
Don't take the paper's word for it. Measure your own model's context decay, then prove your fix actually works.
All 6 modules skeleton-only (question, gate shape, and takeaway shape decided; no authored exercise yet)
- llm-evaluation
- context-engineering
- research
- coding-agents
Why LLMs degrade over long context: positional bias, length-driven context rot, and compaction loss. One real, cited number to start: Chroma's 2025 Context Rot report found accuracy dropped 30+ points when relevant information sat further from the start of context. Taught to people who already work with LLMs daily, through empirical-tier exercises (run the measurement yourself) paired with a conceptual gate. The paper measured someone else's model. This measures yours.
